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Projects Page
This project was completed for the MUSA/Smart Cities Practicum course
(MUSA 801) instructed by Michael Fichman and Matthew Harris. We are
grateful to our instructors for their continued support and feedback. We
would like to give special thanks to KC Filippino and Ben McFarlane from
Hampton Roads Planning District Commission, and Dexter Locke from the
United States Forest Service for providing data, insight, and support
throughout the semester. This project would not have been possible
without them.
View
Dashboard
1.Introduction
1.1 Abstract
This project aims to develop a precision forecast model for land
cover change at the Chesapeake Watershed. By leveraging high-resolution
longitudinal land cover data provided by the Chesapeake Conservancy, the
model will predict land cover conversions from pervious to impervious
surfaces. This forecast will enable land use and environmental planners
to identify where urban growth will occur, propose green infrastructure
accordingly, and prioritize lands for protection. The model will be
generalizable to the county level, incorporating only widely available
inputs, thus allowing any municipality within the Chesapeake basin to
replicate the analysis. This proof-of-concept project will demonstrate
the utility of precision conservation in land protection and green
infrastructure planning and provide a valuable tool for planners and
policymakers across the region.
1.2 Background
The Chesapeake Bay watershed is an ecologically and economically
significant resource, encompassing diverse ecosystems and supporting a
multitude of industries, including agriculture, tourism, and fisheries.
However, the region is facing increasing environmental challenges due to
the combined effects of sea-level rise and land subsidence. As a result,
the area has become the second-most vulnerable region in the nation to
flooding and storm surge, only after New Orleans. Predicting land cover
changes, particularly the conversion from pervious to impervious
surfaces, is crucial in addressing these challenges and informing
climate adaptation and mitigation planning.
Our project focuses on three distinct counties within the Chesapeake
Bay watershed, representing varying development contexts. Portsmouth is
the urban prototype characterized by its dense residential, commercial,
and industrial areas. James City County exemplifies a suburban context,
with a mix of rural, suburban, and urban development and a diverse
landscape encompassing forests, wetlands, and historic sites. Lastly,
Isle of Wight County represents the rural aspect, predominantly
characterized by agriculture, forestry, and extensive natural habitats.
By considering these diverse counties, we can develop a comprehensive
and generalizable model to predict land cover changes across various
regional development scenarios.

1.3 Motivation & Use Case
Building resilient communities is a top priority for the Hampton
Roads Planning District Commission (HRPDC). To support this goal, the
HRPDC has established a green infrastructure plan that seeks to identify
and prioritize a network of valuable conservation lands. This plan aims
to achieve multiple benefits, such as habitat protection, drinking water
supply protection, stormwater management, and recreational
opportunities.
A crucial component of this plan involves developing a model to
forecast potential future growth and identify areas of the green
infrastructure network that are most at risk for development. Our
project aims to create a forecast that enables land use and
environmental planners to pinpoint where urban growth is likely to
occur, propose green infrastructure accordingly, and prioritize lands
for protection.
This proof-of-concept project demonstrates the utility of precision
conservation in land protection and green infrastructure planning,
providing a valuable tool for planners and policymakers across the
region. For example, Andrew, the head of the Green Infrastructure Team
from Chesapeake Conservancy, and his team will use our web app to make
informed decisions on which regions have the highest priority to receive
funding.

2. Data and Methods
2.1 Understanding Land Cover data
The Chesapeake Conservancy provided high-resolution land cover data
for 2013/14 and 2017/18. This vast raster dataset boasts an impressive
1-meter accuracy, offering 900 times more detail than the commonly used
30-meter resolution National Land Cover Dataset. Such a level of detail
is crucial for capturing subtle changes in land cover.
The land cover classification includes pervious surfaces such as tree
canopies and shrubs, which allow water to infiltrate the ground. In
contrast, impervious surfaces encompass categories like roads and
structures that prevent water infiltration, leading to increased runoff
and potential flooding issues. Even though water and wetland are often
considered impervious surfaces, in this study, we classify them as
pervious surfaces due to their dynamic nature, interaction with
groundwater, floodplain connectivity, and the critical functions of
wetlands in water storage and infiltration.

Data
Source
2.2 Ohter data
To understand how landcover change is affected by other
environmental, social and economical factors, we also acquire data from
the following source:
2.3 Unit of analysis
To make it easier for our future analysis, we gathered and resampled
all data to 10 x 10 meters resolution as the basic analysis unit to
ensure consistency.

3. Exploratory Analysis
3.1 Land Cover Type and Change Dynamics
We began by comparing the land cover patterns and changes across the
three counties. This comparison helped us identify each county’s unique
characteristics and trends, providing insights into how urban, suburban,
and rural contexts affected land cover conversion. Land cover changes
are highly related to the existing land cover type. For James City and
Isle of Wight, tree canopy is the land cover type that undergoes the
most change, while for Portsmouth, low vegetation experiences the most
significant change.

3.2 Environmental Factors
Environmental factors played a significant role in land cover
changes. We examined site characteristics that remained constant over
time, such as elevation, slope, and soil type, to understand their
influence on land cover conversion patterns. Understanding these static
factors helped us predict how land cover might evolve in different
environmental settings.
Take Portsmouth county as example, the plot below demonstrates that
the mean slope in areas where land cover changed from pervious to
impervious between 2014 and 2018 (1) is significantly higher than in
areas that did not change or changed in the opposite direction (0).

3.3 Social and Economical Factors
Social and economic factors, which change over time, also impact land
cover changes. We investigated variables such as population growth,
economic development, and demographic changes to understand how these
factors contributed to land cover conversion. By incorporating these
dynamic factors into our analysis, we were able to better forecast land
cover changes based on potential future scenarios.
The plots presented here illustrate that the percentage of white
population change exhibits a different pattern in areas of land cover
change compared to the overall county. This observation indicates that
in Portsmouth County, land cover change is also associated with changes
in census data, particularly the percentage of white population
change.

4. Feature Engineering
4.1 Spatial Effects
Incorporating spatial relationships into our model was crucial for
capturing the spatial influence of original land cover types on land
cover change. We performed focal raster calculations and spatial lag
calculations to create features that accounted for the spatial context
of land cover conversion. These engineered features helped us develop a
more accurate and robust predictive model, capable of capturing the
nuances of land cover change across diverse development contexts.

4.2 Final Features (Need Check!)
Dependent Variable
- Whether the land cover change from impervious to previous
Independent Variables
- Existing land cover types
- Pop change
- Pct of white change
- Unitchange
- Median Household Income change
- Environmental factors
- Elevation (Terrain)
- Slope
- Soil Type
- Socio-economic factors
- Pop change
- Pct of white change
- Unitchange
- Median Household Income change
- Spatial Lag factors
- Pop change
- Pct of white change
- Unitchange
- Median Household Income change
5. Modeling & Evaluation (Need Check!)
5.1 Model Building
To ensure computational efficiency and scalability, we downsampled
the original dataset to 500,000 data points for model building and then
fitted the selected model back to the whole dataset for future
predictions. We employed geo cross-validation at the block group level
to ensure the robustness of our model and avoid overfitting.
Performance evaluation was conducted using the confusion matrix for
binary threshold setting and model selection. This approach allowed us
to assess the accuracy, sensitivity, and specificity of the models and
compare their performance.

5.2 Model Type Selection
Recognizing that different types of models perform well on different
datasets, we experimented with three model types for predicting land
cover change: Random Forest, XGBoost, and Binomial Generalized Linear
Model (GLM). These models were chosen due to their ability to handle
complex interactions and non-linear relationships within the data.
After evaluating the performance of each model type, we selected
Random Forest as the most suitable model for our analysis, as it
demonstrated the best accuracy. Random Forest is an ensemble learning
method that builds multiple decision trees and combines their results to
improve overall accuracy and stability. This model is particularly
well-suited for handling high-dimensional and noisy data, making it an
ideal choice for predicting land cover changes in our study area.

5.3 Refine for better prediction
5.4 Model Evaluation & Validation
roc

5.5 Comparison between Counties

6. Prediction & Error Analysis
By comparing the predicted 2018 land cover change with the observed
change, we can tell that errors in the prediction are likely to be
around the true value, indicating the effectiveness of the model.
Looking at the future prediction of 2021, we can see that many of the
predicted changes are consistent with the observed change. However, the
model has also identified new areas of land cover change. This suggests
that the model is able to detect changes that were not previously
observed, which can be valuable for land management and conservation
efforts.

6. Conclusion
7. Code Appendix
---
title: "Precision Forecasts of Land Cover Change"
subtitle: "Chesapeake Watershed"
author: "Yuewen Dai, Shujing Yi, Xinge Zhang"
date: "2023-04-25"
output: 
  html_document:
    toc: true
    toc_float: true
    code_folding: "hide"
    code_download: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

[Return to MUSA 801 Projects Page](https://pennmusa.github.io/MUSA_801.io/)

This project was completed for the MUSA/Smart Cities Practicum course (MUSA 801) instructed by Michael Fichman and Matthew Harris. We are grateful to our instructors for their continued support and feedback. We would like to give special thanks to KC Filippino and Ben McFarlane from Hampton Roads Planning District Commission, and Dexter Locke from the United States Forest Service for providing data, insight, and support throughout the semester. This project would not have been possible without them.

[View Dashboard](https://yuewendai.github.io/MUSA-Practicum-Web/)

## 1.Introduction
### 1.1 Abstract
This project aims to develop a precision forecast model for land cover change at the Chesapeake Watershed. By leveraging high-resolution longitudinal land cover data provided by the Chesapeake Conservancy, the model will predict land cover conversions from pervious to impervious surfaces. This forecast will enable land use and environmental planners to identify where urban growth will occur, propose green infrastructure accordingly, and prioritize lands for protection. The model will be generalizable to the county level, incorporating only widely available inputs, thus allowing any municipality within the Chesapeake basin to replicate the analysis. This proof-of-concept project will demonstrate the utility of precision conservation in land protection and green infrastructure planning and provide a valuable tool for planners and policymakers across the region.

### 1.2 Background 
The Chesapeake Bay watershed is an ecologically and economically significant resource, encompassing diverse ecosystems and supporting a multitude of industries, including agriculture, tourism, and fisheries. However, the region is facing increasing environmental challenges due to the combined effects of sea-level rise and land subsidence. As a result, the area has become the second-most vulnerable region in the nation to flooding and storm surge, only after New Orleans. Predicting land cover changes, particularly the conversion from pervious to impervious surfaces, is crucial in addressing these challenges and informing climate adaptation and mitigation planning.

Our project focuses on three distinct counties within the Chesapeake Bay watershed, representing varying development contexts. Portsmouth is the urban prototype characterized by its dense residential, commercial, and industrial areas. James City County exemplifies a suburban context, with a mix of rural, suburban, and urban development and a diverse landscape encompassing forests, wetlands, and historic sites. Lastly, Isle of Wight County represents the rural aspect, predominantly characterized by agriculture, forestry, and extensive natural habitats. By considering these diverse counties, we can develop a comprehensive and generalizable model to predict land cover changes across various regional development scenarios.

![](images/HRPDC.jpg)


### 1.3 Motivation & Use Case

Building resilient communities is a top priority for the Hampton Roads Planning District Commission (HRPDC). To support this goal, the HRPDC has established a green infrastructure plan that seeks to identify and prioritize a network of valuable conservation lands. This plan aims to achieve multiple benefits, such as habitat protection, drinking water supply protection, stormwater management, and recreational opportunities.

A crucial component of this plan involves developing a model to forecast potential future growth and identify areas of the green infrastructure network that are most at risk for development. Our project aims to create a forecast that enables land use and environmental planners to pinpoint where urban growth is likely to occur, propose green infrastructure accordingly, and prioritize lands for protection. 

This proof-of-concept project demonstrates the utility of precision conservation in land protection and green infrastructure planning, providing a valuable tool for planners and policymakers across the region. For example, Andrew, the head of the Green Infrastructure Team from Chesapeake Conservancy, and his team will use our web app to make informed decisions on which regions have the highest priority to receive funding.

![](images/use_case.png)


## 2. Data and Methods

### 2.1 Understanding Land Cover data
The Chesapeake Conservancy provided high-resolution land cover data for 2013/14 and 2017/18. This vast raster dataset boasts an impressive 1-meter accuracy, offering 900 times more detail than the commonly used 30-meter resolution National Land Cover Dataset. Such a level of detail is crucial for capturing subtle changes in land cover.

The land cover classification includes pervious surfaces such as tree canopies and shrubs, which allow water to infiltrate the ground. In contrast, impervious surfaces encompass categories like roads and structures that prevent water infiltration, leading to increased runoff and potential flooding issues. Even though water and wetland are often considered impervious surfaces, in this study, we classify them as pervious surfaces due to their dynamic nature, interaction with groundwater, floodplain connectivity, and the critical functions of wetlands in water storage and infiltration. 

![](images/Data.png)

[Data Source](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/)


### 2.2 Ohter data
To understand how landcover change is affected by other environmental, social and economical factors, we also acquire data from the following source: 

* [DEM ( 1 arc-second) from USGS](https://apps.nationalmap.gov/downloader/)

* [Soil Data from the Web Soil Survey](https://websoilsurvey.nrcs.usda.gov/app/)

* census tract-level data (2014, 2018, 2021) from four years of the American Community Survey (ACS) 

### 2.3 Unit of analysis
To make it easier for our future analysis, we gathered and resampled all data to 10 x 10 meters resolution as the basic analysis unit to ensure consistency.


![](images/method.png)




## 3. Exploratory Analysis

### 3.1 Land Cover Type and Change Dynamics
We began by comparing the land cover patterns and changes across the three counties. This comparison helped us identify each county's unique characteristics and trends, providing insights into how urban, suburban, and rural contexts affected land cover conversion. Land cover changes are highly related to the existing land cover type. For James City and Isle of Wight, tree canopy is the land cover type that undergoes the most change, while for Portsmouth, low vegetation experiences the most significant change.

![](images/landcover.png)

### 3.2 Environmental Factors

Environmental factors played a significant role in land cover changes. We examined site characteristics that remained constant over time, such as elevation, slope, and soil type, to understand their influence on land cover conversion patterns. Understanding these static factors helped us predict how land cover might evolve in different environmental settings.

Take Portsmouth county as example, the plot below demonstrates that the mean slope in areas where land cover changed from pervious to impervious between 2014 and 2018 (1) is significantly higher than in areas that did not change or changed in the opposite direction (0).

![](images/environment.png)

### 3.3 Social and Economical Factors
Social and economic factors, which change over time, also impact land cover changes. We investigated variables such as population growth, economic development, and demographic changes to understand how these factors contributed to land cover conversion. By incorporating these dynamic factors into our analysis, we were able to better forecast land cover changes based on potential future scenarios.

The plots presented here illustrate that the percentage of white population change exhibits a different pattern in areas of land cover change compared to the overall county. This observation indicates that in Portsmouth County, land cover change is also associated with changes in census data, particularly the percentage of white population change.


![](images/social.png)



## 4. Feature Engineering

### 4.1 Spatial Effects
Incorporating spatial relationships into our model was crucial for capturing the spatial influence of original land cover types on land cover change. We performed focal raster calculations and spatial lag calculations to create features that accounted for the spatial context of land cover conversion. These engineered features helped us develop a more accurate and robust predictive model, capable of capturing the nuances of land cover change across diverse development contexts.

![](images/feature_engineering.png)

### 4.2 Final Features (Need Check!)
#### Dependent Variable
* Whether the land cover change from impervious to previous 

#### Independent Variables

* Existing land cover types 
    + Pop change
    + Pct of white change 
    + Unitchange
    + Median Household Income change   

* Environmental factors
    + Elevation (Terrain)
    + Slope
    + Soil Type

* Socio-economic factors 
    + Pop change
    + Pct of white change 
    + Unitchange
    + Median Household Income change    

* Spatial Lag factors 
    + Pop change
    + Pct of white change 
    + Unitchange
    + Median Household Income change 



## 5. Modeling & Evaluation (Need Check!)

### 5.1 Model Building
To ensure computational efficiency and scalability, we downsampled the original dataset to 500,000 data points for model building and then fitted the selected model back to the whole dataset for future predictions. We employed geo cross-validation at the block group level to ensure the robustness of our model and avoid overfitting.

Performance evaluation was conducted using the confusion matrix for binary threshold setting and model selection. This approach allowed us to assess the accuracy, sensitivity, and specificity of the models and compare their performance.

![](images/sample.png)

### 5.2 Model Type Selection

Recognizing that different types of models perform well on different datasets, we experimented with three model types for predicting land cover change: Random Forest, XGBoost, and Binomial Generalized Linear Model (GLM). These models were chosen due to their ability to handle complex interactions and non-linear relationships within the data.

After evaluating the performance of each model type, we selected Random Forest as the most suitable model for our analysis, as it demonstrated the best accuracy. Random Forest is an ensemble learning method that builds multiple decision trees and combines their results to improve overall accuracy and stability. This model is particularly well-suited for handling high-dimensional and noisy data, making it an ideal choice for predicting land cover changes in our study area.

![](images/model_compare.png)

### 5.3 Refine for better prediction



### 5.4 Model Evaluation & Validation

roc

![](images/sen.png)


### 5.5 Comparison between Counties

![](images/model_importance.png)


## 6. Prediction & Error Analysis

By comparing the predicted 2018 land cover change with the observed change, we can tell that errors in the prediction are likely to be around the true value, indicating the effectiveness of the model. Looking at the future prediction of 2021, we can see that many of the predicted changes are consistent with the observed change. However, the model has also identified new areas of land cover change. This suggests that the model is able to detect changes that were not previously observed, which can be valuable for land management and conservation efforts.


![](images/predic1000.png)



## 6. Conclusion

## 7. Code Appendix

3.3 Social and Economical Factors
Social and economic factors, which change over time, also impact land cover changes. We investigated variables such as population growth, economic development, and demographic changes to understand how these factors contributed to land cover conversion. By incorporating these dynamic factors into our analysis, we were able to better forecast land cover changes based on potential future scenarios.
The plots presented here illustrate that the percentage of white population change exhibits a different pattern in areas of land cover change compared to the overall county. This observation indicates that in Portsmouth County, land cover change is also associated with changes in census data, particularly the percentage of white population change.